1. Field of the Invention
The present invention relates generally to semiconductor substrate cleaning systems. More particularly, the present invention relates to an automated system and method for controlling a dense phase fluid cleaning system by analyzing contaminant removal.
2. Description of Related Art
Generally, semiconductor substrates, such as silicon wafers, are processed into semiconductor chips by sequentially exposing each substrate to a number of individual processes, such as photo masking, etching, implantation, and cleaning. Cleaning typically requires cleaning resist and/or etching residue from the surface of the substrate.
Generally, there are two methods for cleaning the surface of a substrate, namely, wet and dry processing. Wet processing consists of a series of steps of spraying and/or immersing the substrate in expensive chemical solutions or solvents. These conventional solvent aided cleaning processes are currently being re-evaluated due to environmental concerns. In addition, recent environmental legislation mandates that many of the organic solvents used in wet processes be banned or their use severely limited. Dry processing, on the other hand, consists of a series of steps that use gases instead of wet chemical solutions to clean the substrate. For example, ashing using an O2 plasma. However, such processes often leave a residue after dry cleaning, which is unacceptable because such residue may cause device failures or limit operation efficiency.
More recently, dense phases gases or fluids, such as carbon dioxide (CO2) with or without co-solvents or surfactants, have been introduced to remove etch residue and/or photoresist from semiconductor substrates. A dense phase fluid is a gas compressed to either supercritical or subcritical conditions to achieve liquid like densities. These dense phase fluids or fluid mixtures are also referred to as dense fluids. Unlike organic solvents, such as n-hexane or 1,1,1 trichloroethane, dense fluids exhibit unique physical and chemical properties such as low surface tension, low viscosity, and variable solute carrying capacity.
Cleaning with dense phase fluids is desirable, as such fluids retain the properties of a liquid, but have the diffusivity and viscosity of a gas. In addition, dense phase fluid cleaning technology can be applied in many industrial processes to significantly reduce or eliminate the use of hazardous chemicals, to conserve natural resources such as water, and to accomplish tasks previously not possible, such as rapid precision cleaning of small features of semiconductor devices (e.g., resist images, VLSI (Very Large Scale Integration) topographical features such as vias, etc.). Supercritical fluids act as a solvent to remove contaminants from the wafer surface and effectively clean the surface of the substrate. However, the cleaning effectiveness of supercritical dense phase fluids is typically enhanced by the addition of chemical agents or co-solvents that react with materials used in semiconductor manufacturing. As the addition of chemical agents, such as co-solvents, is typically not entirely eliminated using dense phase fluid cleaning, a system and method that continually monitors chemical usage during cleaning to keep such chemical agents to allowable standards would be highly desirable.
Moreover, dense phase fluid cleaning has a multitude of adjustable process parameters, such as pressure, temperature, process times, amount of co-solvent used, etc. Accordingly, it is difficult to determine the optimal process parameters for cleaning. Therefore, a system and method that optimizes such process parameters would be highly desirable.
To further complicate matters, every substrate being cleaned has different topographical features and a different composition. Therefore, there are generally no ultimate predefined process parameters that can accurately account for all substrates to remove all contaminants. Accordingly, a system and method that automatically accounts for the variations of each substrate, in real time, would be highly desirable.